![]() Though the solution to the problem is not yet found, as even the pre-caching takes ~52 sec, when done by vmtouch, the reason for the problem is related to the caching of memory. Pyarrow provides similar array and data type support as NumPy including first-class. As part of the test I cleared cache (echo 3 > /proc/sys/vm/drop_caches), which results in longer time for the copy of memmap array to numpy array (to volatile memory).Īs part of confirmation of the issue, when I pre-cache the binary files into memory using vmtouch it takes ~3 sec for the copy (memmap to numpy array) to take place. If same file set is given twice, memory copy takes ~2 sec, but when a different set of files are given the copying takes ~72 sec.Īfter further investigation, I found that this problem indeed stems from caching of memory. Then you Google, and within 2 minutes you are done I love this types of articles. Since Index is immutable, the underlying. Sometimes you think you’re going to spend at least 20 minutes researching a subject. This is the function where the files are read and the data is separated from the header. While Index objects are copied when deepTrue, the underlying numpy array is not copied for performance reasons. The binary file input has 32 bytes header and 1024 bytes data, the focus is on reading the latter memmap array to numpy array. Return tempcomf_X, tempcomf_Y, tempcomf1_X, tempcomf1_Y Tempcomf1_Y= np.array(tempcomf1,order = 'F') Summary: in this tutorial, youll learn how to use the NumPy copy() method to. Tempcomf1_X= np.array(tempcomf1,order = 'F') to help us pay for the web hosting fee and CDN to keep the website running. Tempcomf_Y = np.array(tempcomf, order = 'F') Tempcomf_X = np.array(tempcomf, order = 'F') Print('Time take for memarray copy.'+str(time.time()-t_1)) Is it possible to convert a torch tensor to a numpy array using the GPU for faster performance Currently, I am using input input. Here is the function used: cpdef tuple decrypt_file(file_name, file_name1):Ĭdef np.ndarray tempcomf = np.zeros((templen, 1024),dtype=np.int8)Ĭdef np.ndarray tempcomf1 = np.zeros((templen1, 1024),dtype=np.int8)ĭt = np.dtype()Ĭomf = np.memmap(file_name, dtype = dt, mode = 'c') What is the efficient and fastest way of copying the memmap to numpy array?Īny suggestions on the same are appreciated.Ĭode used: comf = np.memmap(file_name, dtype = dt, mode = 'c')Ĭomf1 = np.memmap(file_name1, dtype = dt, mode = 'c')Ĭdef np.ndarray tempcomf = np.zeros((templen, 1024), dtype = np.int8)Ĭdef np.ndarray tempcomf1 = np.zeros((templen1, 1024), dtype = np.int8) I am implementing this as a Cython function, though the copying of memmap array to numpy array is quite fast (~3 sec) when same set of files are processed twice, it takes large amount of time if new files are to be read (~71 sec), possibly because of cache memory, this is the same with numpy fromfile as well. I want to minimise the time required by this IO process, in the code. I have multiple binary (structured) file, each of 2GB, which I am currently reading in pair, using memmap to cross-correlate the same.
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